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Continuous Opinion Dynamics of Multidimensional Allocation Problems under Bounded Confidence. More dimensions lead to better chances for consensus

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  • Lorenz, Jan

    (Bremen International Graduate School of Social Sciences (BIGSSS))

Abstract

We study multidimensional continuous opinion dynamics, where opinions are nonnegative vectors which components sum up to one. Examples of such opinions are budgets or other allocation vectors which display a distribution of a fixed amount of ressource to n projects. We use the opinion dynamics models of Deffuant-Weisbuch and HegselmannKrause, which both extend naturally to more dimensional opinions. They both rely on bounded confidence of the agents and differ in their communication regime. We show detailed simulation results regarding n = 2, . . . , 8 and the bound of confidence . Number, location and size of opinion clusters in the stabilized opinion profiles are of interest. Known differences of both models repeat under higher opinion dimensions: Higher number of clusters and more minor clusters in the Deffuant-Weisbuch model, meta-stable states in the Hegselmann-Krause model. But surprisingly, higher dimensions lead to better chances for a vast majority consensus even for lower bounds of confidence. On the other hand, the number of minority clusters rises with n, too.

Suggested Citation

  • Lorenz, Jan, 2006. "Continuous Opinion Dynamics of Multidimensional Allocation Problems under Bounded Confidence. More dimensions lead to better chances for consensus," European Journal of Economic and Social Systems, Lavoisier, vol. 19(2), pages 213-227.
    • Handle: RePEc:ris:ejessy:0096
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    Cited by:

    1. Marco D'Errico & Gulnur Muradoglu & Silvana Stefani & Giovanni Zambruno, 2014. "Opinion Dynamics and Price Formation: a Nonlinear Network Model," Papers 1408.0308, arXiv.org.
    2. Shane T. Mueller & Yin-Yin Sarah Tan, 2018. "Cognitive perspectives on opinion dynamics: the role of knowledge in consensus formation, opinion divergence, and group polarization," Journal of Computational Social Science, Springer, vol. 1(1), pages 15-48, January.

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